Regional and Global Impacts of Land Cover Change and Sea Surface Temperature Anomalies

2009 ◽  
Vol 22 (12) ◽  
pp. 3248-3269 ◽  
Author(s):  
Kirsten L. Findell ◽  
Andrew J. Pitman ◽  
Matthew H. England ◽  
Philip J. Pegion

Abstract The atmospheric and land components of the Geophysical Fluid Dynamics Laboratory’s (GFDL’s) Climate Model version 2.1 (CM2.1) is used with climatological sea surface temperatures (SSTs) to investigate the relative climatic impacts of historical anthropogenic land cover change (LCC) and realistic SST anomalies. The SST forcing anomalies used are analogous to signals induced by El Niño–Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the background global warming trend. Coherent areas of LCC are represented throughout much of central and eastern Europe, northern India, southeastern China, and on either side of the ridge of the Appalachian Mountains in North America. Smaller areas of change are present in various tropical regions. The land cover changes in the model are almost exclusively a conversion of forests to grasslands. Model results show that, at the global scale, the physical impacts of LCC on temperature and rainfall are less important than large-scale SST anomalies, particularly those due to ENSO. However, in the regions where the land surface has been altered, the impact of LCC can be equally or more important than the SST forcing patterns in determining the seasonal cycle of the surface water and energy balance. Thus, this work provides a context for the impacts of LCC on climate: namely, strong regional-scale impacts that can significantly change globally averaged fields but that rarely propagate beyond the disturbed regions. This suggests that proper representation of land cover conditions is essential in the design of climate model experiments, particularly if results are to be used for regional-scale assessments of climate change impacts.

2017 ◽  
Vol 14 (22) ◽  
pp. 5053-5067 ◽  
Author(s):  
Wei Li ◽  
Philippe Ciais ◽  
Shushi Peng ◽  
Chao Yue ◽  
Yilong Wang ◽  
...  

Abstract. The use of dynamic global vegetation models (DGVMs) to estimate CO2 emissions from land-use and land-cover change (LULCC) offers a new window to account for spatial and temporal details of emissions and for ecosystem processes affected by LULCC. One drawback of LULCC emissions from DGVMs, however, is lack of observation constraint. Here, we propose a new method of using satellite- and inventory-based biomass observations to constrain historical cumulative LULCC emissions (ELUCc) from an ensemble of nine DGVMs based on emerging relationships between simulated vegetation biomass and ELUCc. This method is applicable on the global and regional scale. The original DGVM estimates of ELUCc range from 94 to 273 PgC during 1901–2012. After constraining by current biomass observations, we derive a best estimate of 155 ± 50 PgC (1σ Gaussian error). The constrained LULCC emissions are higher than prior DGVM values in tropical regions but significantly lower in North America. Our emergent constraint approach independently verifies the median model estimate by biomass observations, giving support to the use of this estimate in carbon budget assessments. The uncertainty in the constrained ELUCc is still relatively large because of the uncertainty in the biomass observations, and thus reduced uncertainty in addition to increased accuracy in biomass observations in the future will help improve the constraint. This constraint method can also be applied to evaluate the impact of land-based mitigation activities.


2012 ◽  
Vol 3 (2) ◽  
pp. 597-641 ◽  
Author(s):  
A. J. Pitman ◽  
N. de Noblet-Ducoudré ◽  
F. B. Avila ◽  
L. V. Alexander ◽  
J.-P. Boisier ◽  
...  

Abstract. The impact of historical land use induced land cover change (LULCC) on regional-scale climate extremes is examined using four climate models within the Land Use and Climate, IDentification of robust impacts project. To assess those impacts, multiple indices based on daily maximum and minimum temperatures and daily precipitation were used. We contrast the impact of LULCC on extremes with the impact of an increase in atmospheric CO2 from 280 ppmv to 375 ppmv. In general, changes in both high and low temperature extremes are similar to the simulated change in mean temperature caused by LULCC and are restricted to regions of intense modification. The impact of LULCC on both means and on most temperature extremes is statistically significant. While the magnitude of the LULCC induced change in the extremes can be of similar magnitude to the response to the change in CO2, the impacts of LULCC are much more geographically isolated. For most models the impacts of LULCC oppose the impact of the increase in CO2 except for one model where the CO2-caused changes in the extremes is amplified. While we find some evidence that individual models respond consistently to LULCC in the simulation of changes in rainfall and rainfall extremes, LULCC's role in affecting rainfall is much less clear and less commonly statistically significant, with the exception of a consistent impact over South East Asia. Since the simulated response of mean and extreme temperature to LULCC is relatively large, we conclude that unless this forcing is included we risk erroneous conclusions regarding the drivers of temperature changes over regions of intense LULCC.


2021 ◽  
Vol 12 (2) ◽  
pp. 66-74
Author(s):  
Ricky Anak Kemarau ◽  
Oliver Valentine Eboy

Wetlands are a vital component of land cover in reducing impacts caused by urban heat effects and climate change. Remote sensing technology provides historical data that can study the impact of development on the environment and local climate. The studies of wetland in reducing Land Surface Temperature (LST) in a tropical climate are still lacking. The objective of the study is to examine the influence of land cover change wetland and vegetation on land surface temperature between the years 1988 and 2019. First of all, step, pre-processing, namely geometric correction, atmosphere correction, and radiometric correction, were performed before retrieval of the LST dataset from thermal band Landsat 5 and 8. Then, Iso Cluster, unsupervised was chosen to produce the land cover map for 1988 and 2019. Geographical Information System (GIS) technology was utilized to determine changes to land cover and LST change between the years 1988 and 2019. With GIS technology, a study of the impact of wetland deforestation on local temperatures at a local scale was carried out. Next to that, correlations between LST and the wetland were analyzed. The results indicated the different land cover between the years 1988 and 2019. The areas of land cover for wetland and vegetation decrease and while area of urban increased. The land cover changed the influences of LST significantly in the study area. The LST increased with the decreasing in areas wetland areas for every 5-kilometer square (km²) wetland lost an increase in 1-degree Celsius of LS was estimated. The size of wetland influence on LST was significant. Wetland and vegetation function in reducing the urban heat island effect was vital in providing a comfortable environment to the Kuching population and indirectly reduce the demand for power energy.


PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7523 ◽  
Author(s):  
Chun Xia Liang ◽  
Floris F. van Ogtrop ◽  
R. Willem Vervoort

Analysis of observational data to pinpoint impact of land cover change on local rainfall is difficult due to multiple environmental factors that cannot be strictly controlled. In this study we use a statistical approach to identify the relationship between removal of tree cover and rainfall with data from best available sources for two large areas in Australia. Gridded rainfall data between 1979 and 2015 was used for the areas, while large scale (exogenous) effects were represented by mean rainfall across a much larger area and climatic indicators, such as Southern Oscillation Index and Indian Ocean Dipole. Both generalised additive modelling and step trend tests were used for the analysis. For a region in south central Queensland, the reported change in tree clearing between 2002–2005 did not result in strong statistically significant precipitation changes. On the other hand, results from a bushfire affected region on the border of New South Wales and Victoria suggest significant changes in the rainfall due to changes in tree cover. This indicates the method works better when an abrupt change in the data can be clearly identified. The results from the step trend test also mainly identified a positive relationship between the tree cover and the rainfall at p < 0.1 at the NSW/Victoria region. High rainfall variability and possible regrowth could have impacted the results in the Queensland region.


2020 ◽  
Author(s):  
Christina Asmus ◽  
Peter Hoffmann ◽  
Diana Rechid ◽  
Jürgen Böhner

&lt;p&gt;&lt;span&gt;Large parts of the earth&amp;#8217;s land surface are modified by humans. Since the land surface and the atmosphere are constantly in energy exchange and in interactions with each other, anthropogenic modifications of the land&amp;#8217;s surface can lead to effects on the climate. The objective of this study is to quantify and investigate the effects and feedbacks of irrigation on the local to regional climate. Irrigation is a land use practice, which does not change the land cover type but changes the biophysical properties of the land&amp;#8217;s surface and the soil and thus alters energy and moisture fluxes. These local to regional process responses, detectable in different meteorological variables, are investigated using the regional climate model REMO. High resolution simulations at convection permitting scales will be performed in order to particularly investigate irrigation effects on the spatiotemporal behavior of moist convection. Newly developed parameterizations of different types of irrigation are tested on the example of a northern Italian model domain, where cropland and rice paddies are the dominating land cover. The focus of the sensitivity study is on the impact of the parameterizations on the surface moisture and energy balance as well as on heavy rainfall events. &lt;/span&gt;&lt;/p&gt;


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Anmin Fu ◽  
Yulin Cai ◽  
Tao Sun ◽  
Feng Li

Great efforts have been made to curb soil erosion and restore the natural environment to Inner Mongolia in China. The purpose of this study is to evaluate the impact of returning farmland to the forest on soil erosion on a regional scale. Considering that rainfall erosivity also has an important impact on soil erosion, the effect of land use and land cover change (LUCC) on soil erosion was evaluated through scenario construction. Firstly, the universal soil loss equation (USLE) model was used to evaluate the actual soil erosion (2001 and 2010). Secondly, two scenarios (scenario 1 and scenario 2) were constructed by assuming that the land cover and rainfall-runoff erosivity are fixed, respectively, and soil erosion under different scenarios was estimated. Finally, the effect of LUCC on soil erosion was evaluated by comparing the soil erosion under actual situations with the hypothetical scenarios. The results show that both land use/cover change and rainfall-runoff erosivity change have significant effects on soil erosion. The land use and land cover change initiated by the ecological restoration projects have obviously reduced the soil erosion in this area. The results also reveal that the method proposed in this paper is helpful to clarify the influencing factors of soil erosion.


2017 ◽  
Author(s):  
Wei Li ◽  
Philippe Ciais ◽  
Shushi Peng ◽  
Chao Yue ◽  
Yilong Wang ◽  
...  

Abstract. The use of dynamic global vegetation models (DGVMs) to estimate CO2 emissions from land-use and land-cover change (LULCC) offers a new window to account for spatial and temporal details of emissions, and for ecosystem processes affected by LULCC. One drawback of DGVMs however is their large uncertainty. Here, we propose a new method of using satellite- and inventory-based biomass observations to constrain historical cumulative LULCC emissions (EcLUC) from an ensemble of nine DGVMs based on emerging relationships between simulated vegetation biomass and EcLUC. This method is applicable at global and regional scale. Compared to the large range of EcLUC in the original ensemble (94 to 273 Pg C) during 1901–2012, current biomass observations allow us to derive a new best estimate of 155 ± 50 (1-σ Gaussian error) Pg C. The constrained LULCC emissions are higher than prior DGVM values in tropical regions, but significantly lower in North America. Our approach of constraining cumulative LULCC emissions based on biomass observations reduces the uncertainty of the historical carbon budget, and can also be applied to evaluate the impact of land-based mitigation activities.


2022 ◽  
Author(s):  
TC Chakraborty ◽  
Yun Qian

Abstract Although the influence of land use/land cover change on climate has become increasingly apparent, cities and other built-up areas are usually ignored when estimating large-scale historical climate change or for future projections since cities cover a small fraction of the terrestrial land surface1,2. As such, ground-based observations of urban near-surface meteorology are rare and most earth system models do not represent historical or future urban land cover3–7. Here, by combining global satellite observations of land surface temperature with historical estimates of built-up area, we demonstrate that the urban temperature signal on continental- to regional-scale warming has become non-negligible, especially for rapidly urbanizing regions in Asia. Consequently, expected urban expansion over the next century suggest further increased urban influence on surface climate under all future climate scenarios. Based on these results, we argue that, in line with other forms of land use/land cover change, urbanization should be explicitly included in future climate change assessments. This would require extensive model development to incorporate urban extent and biophysics in current-generation earth system models to quantify potential urban feedbacks on the climate system at multiple scales.


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